High-quality image data is pivotal for accurate deep learning models. For certain tasks, it is even more important than neural architectures and hyperparameters. Data collection, cleaning, and annotation have become a nightmare lately generally costing millions of dollars. This is majorly due to challenges in data diversity, image quality, pixel-perfect manual annotations, as well as licensing [1]}, and security concerns. This is exacerbated by the fact that writing instructions for manual annotation are not a trivial task as it is not always possible to explain concisely the requirements. To this end, we propose a universal framework to generate countless 3D scenes in different environments along with annotated images for various autonomous tasks for cars, drones, and indoor robots. This would enable deep learning frameworks to gather photorealistic data much more efficiently in terms of cost, speed, labor and variety.
